A knowledge construction methodology to automate case‐based learning using clinical documents
Open accessAbstract
The case‐based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer‐based domain knowledge to medical students for solving real‐world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine‐readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM‐CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM‐CD methodology, an interactive case‐based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.
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Three additional studies explored the integration of AI in case-based learning.
“Three additional studies explored the integration of AI in case-based learning [150–152].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [151]
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Artificial intelligence in undergraduate medical education: an updated scoping review
Simoni, J., Urtubia-Fernandez, J., Mengual, E., Simoni, D. A., Royo, M., Egaña-Yin, D., Hertog, O. L. A., López-Ortiz, L., Muñoz-Tomás, A., Santiago-Martínez, P., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08188-2 · Cited by 62 · see citing works
- Authors
- Ali, M., Hussain, J., Lee, S., Kang, B. H., & Sattar, K.
- Affiliations
- University of Tasmania (AU)
- Kyung Hee University (KR)
- Pir Mehr Ali Shah Arid Agriculture University (PK)
- Corresponding author
- Lee, Sungyoung
- Venue
- Expert Systems
- Publisher
- Wiley
- Published
- 2019-04-10
- Volume
- 37
- Issue
- 1
- ISSN
- 0266-4720 (ISSN-L)
- 1468-0394
- OpenAlex
- W2938378585
- Cited by
- 4
- References
- 59
- Citations by year
- 2025: 2 · 2021: 1 · 2019: 1
- FWCI
- 0.17
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Locations
- Expert Systems (publishedVersion)
- UTAS Research Repository (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Institute for Information & Communications Technology Promotion (IITP), Ministry of Science and ICT, Republic of Korea (IITP-2017-0-00655, IITP-2017-0-01629)
- Ministry of Trade, Industry & Energy (MI), Republic of Korea (10052955)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- case-based learning, medical documents, unstructured text, medical education, ontology construction, coherence measures, knowledge engineering
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